Camouflage, which can be broadly defined as apparatus and methods for protecting covert mobile and stationary objects from detection, is an important requirement for many civilian and military applications. Current approaches to camouflage, also known as “signature management,” that are used to shield vehicles and other mobile and stationary ground-based assets typically include a camouflage cover that extends over the ground-based asset and presents colors and marking patterns that attempt to match colors and patterns in the visible background.
Often, so-called “garnish” features are included in the camouflage cover, where the term “garnish” refers to any feature that attempts to emulate the texture of the surrounding environment, especially the texture of leaves and other vegetation. Garnish can include “virtual” garnish, such as printed fabric or nets that generate the appearance of a texture with a depth of shadow created beneath it. The garnish effect can also include “physical” garnish such as artificial folds and/or flaps of fabric, referred to herein as “petalation,” that is added to or imposed on the fabric or nets of the camouflage cover. Such physical garnish can be highly effective in removing flat specular surfaces and/or providing self-shadowing and an appropriate range of reflectance that simulates vegetation.
Advances in various method of detection have made it increasingly difficult to successfully camouflage covert objects. In particular, the development and proliferation of advanced ground-observation and ground-scanning technologies has made it increasingly difficult to avoid detection of covert ground-based objects from above. Threats that are not adequately addressed by current camouflage approaches include advanced radar methods such as Side Aperture Radar (SAR) and the laser range finding analog of radar known as “LiDAR,” as well as apparatus that detect EM signatures emitted by communications equipment and other electronic devices. Often, it is also desirable to protect assets from infrared (“IR”) detection by shielding the heat emissions of an asset.
Methods for avoiding detection of covert objects by radar include covering the covert object with materials and coatings that scatter and/or absorb radar waves, as well as by configuring the covert object with structures that further scatter radar waves. Attempts have also been made to mask EM signatures within selected wavelength ranges emitted by communications equipment and other electronic devices.
Infrared signature management can include both passive shielding, using various types of insulation materials, as well as active shielding, for example by circulating a cooled liquid such as water through a vascular system provided in the camouflage cover. Attempts have been made using these methods to mask thermal signatures to a level that is below the background IR.
When camouflage is provided for vehicles and/or other portable ground-based assets that are traveling through a variable environment, such as a region of high vegetation, it can be necessary to maintain and transport a large number of camouflage covers that display a variety of different patterns and colors in an attempt to match the highly variable background. This can be a burdensome requirement, which is rendered even more costly and bulky when the camouflage covers also include vascular systems for heat shielding. Unfortunately, even when a variety of different camouflage covers are provided, it is inevitable that vegetative environments will be encountered for which none of the provided covers provides an ideal match to the surrounding colors and patterns.
Furthermore, as commercially available detecting instrumentation has grown smaller and costs have been reduced, it has become practical to employ sophisticated analysis of the visual, UV, and IR spectral signatures of the environment in detecting camouflaged assets. As a result, in a growing number of hostile situations it is no longer sufficient for camouflage to simply mask heat emissions and match the surrounding colors and visual patterns, because hostile forces are able to use this new instrumentation to detect differences and variations in the infrared (“IR”) and ultraviolet (“UV”) absorptions and emissions in a scene as a function of IR and UV wavelength. This “spectroscopic” approach to defeating camouflage is sensitive to differences in the chemical makeup of the scene, and not simply to visible colors and energy emission levels.
As a result, existing camouflage systems can often be defeated through remote sensing using multispectral, hyperspectral or thermal imaging instruments, and for this reason it is becoming increasingly important for camouflage to emulate the chemical, or “spectral” signature of the surrounding environment.
Typical leaf vegetation is composed of between 90% and 95% water, with the remainder being a combination of biochemical components, namely:                dry matter (cell walls): 5-10%        cellulose: 15-30%        hemicellulose: 10-30%        proteins: 10-20%        lignin: 5-15%        starch: 0.2-2.7%-sugar-etc.        
Since water is transparent in the visible range, the visible appearance of vegetation is mainly dominated by the biochemical components in the upper surfaces of leaves or other plant structures. These biochemical components also contribute to the IR spectral signature of the vegetation. As illustrated in FIGS. 1 and 2A, natural vegetation has a distinctive pattern of absorption that spans the visible and infrared wavelength regions. For example, with reference to FIGS. 2A, and 2B, it can be important for a camouflage system to match the sharp reflection in the near IR 700 nm region that is associated with vegetation chromophores.
Also, matching absorptions in the mid-IR range can be critical. For example, the following are FTIR absorption regions that are due to common functional groups found in peat (source: Aiken, 1985):
TABLE 1Wavenumbercm-1Associated functional group750-880Hydrogen-bonded OH stretching of carboxylic groups.1040-1090C—O stretching of alcoholic compounds, polysaccharides.1137-1280C—O stretching of esters, ethers and phenols.1332-1390Salts of carboxylic acids1390-1400OH deformations and C—O stretching of phenolic OH, C—H deformation of CH3 groups.1420-1470Aliphatic C—H deformation.1515C═C stretching in benzene and/or pyridine.1585-1640C═O stretching of double bonds in cyclic and acyclic compounds.1640-1725C═O stretching of carboxylic acids.1850-2500Carboxylate ions.2850-2950Aliphatic C—H, C—H2, C—H3 stretching. The absorption at the range from 3000 to 2800 cm-1 shows presence of alkanes. Twin peaks at about 2920 and 2850 cm-1 are found because of symmetrical and asymmetrical stretching of aliphatic C—H3030-3077Aromatic C—H stretching.3300-3670Hydrogen-bonded OH groups.
In addition to bio-chromophores, the water that is included in vegetation also plays a significant role in determining the IR spectral signature, including the water that is transpired by the vegetation into the surrounding atmosphere, typically from the rear surfaces of leaves or other plant structures.
It is not yet practical to use interferometric instruments such as FTIR laboratory spectrometers for spatial remote sensing. Instead, simpler instruments are typically used to “fingerprint” the spectral signature by measuring absorption or reflectance at key wavelengths. Nevertheless, interferometric laboratory instruments can be very useful for defining and characterizing the IR spectral signatures of natural materials. For example, the absorption bands from water at 1400 nm and 1950 nm (1.4 μm and 1.95 μm) are clearly visible in FIG. 2B.
FIG. 3 presents an FTIR spectrum of water (source: NIST chemistry workbook, http://webbook.nist.gov/cgi/cbook.cgi?ID=C7732185&Units=SI&Type=IR-SPEC&Index=1#IR-SPEC, from the Coblentz Society's evaluated infrared reference spectra collection), and FIG. 4 presents a more detailed FTIR spectra of water shown with and without atmospheric correction. FIG. 5 compares the water FTIR spectrum of FIG. 3 with a basella rubra leaf FTIR spectrum at room temperature.
There are significant differences between the FTIR spectra of various types of leaves and other vegetation, as illustrated in FIGS. 5-10. FIG. 6 is an FTIR spectrum of peat (source: Comparative Study of Peat Composition by using FT-IR Spectroscopy; Janis Krumins, Maris Klavins, Valdis Seglins; Material Science and Applied Chemistry; 26; (2012)). FIG. 7 presents FTIR spectra of strawberry leaf powder before and after ammonium adsorption. FIG. 8 is an FTIR spectrum of an extract powder of Olea Europaea leaves. FIGS. 9 and 10 are FT-IR spectra of native cellulose (FIG. 9) and regenerated cellulose (FIG. 10).
Of course, the hydration of a vegetative canopy can vary with the soil water availability. FIG. 11 presents FTIR spectra that illustrate changes in reflectance for a magnolia leaf as the leaf dries and the water content declines. The approximate absorption ranges for the three major vegetative IR absorption components, chlorophyll A, chlorophyll B, and carotenoids, are shown in FIG. 12.
These differences between the spectral signatures of different vegetative environments increase the challenge and the importance of accurately matching camouflage to the spectral signature of the specific vegetative environment in which it is used.
What is needed, therefore, is a camouflage system and method that can protect an asset from spectrum signature detection by emulating the visual, and IR spectral signatures of surrounding vegetation, and that can adapt to changes in the spectral background signature in a variable background environment, without requiring maintenance and transport of multiple, redundant camouflage covers.